Intersection of Two Arrays II (Hash Map)
Given two arrays, return an array of their intersection. Each element in the result should appear as many times as it shows in both arrays. Use a Hash Map to store frequencies.
Why Interviewers Ask This
Amazon interviewers ask this to evaluate your mastery of frequency counting and hash map efficiency. They specifically test if you can handle duplicate elements correctly, a common edge case in data processing. This assesses your ability to choose the optimal O(n) time complexity solution over brute force methods, aligning with their bias for action and inventing simple, scalable algorithms.
How to Answer This Question
1. Clarify Requirements: Immediately confirm if duplicates are allowed and what happens if arrays have different lengths. Mention that the result must reflect the minimum count of each element found in both arrays.
2. Propose the Strategy: State clearly that you will use a Hash Map (or Frequency Counter) to store counts from the smaller array first to optimize space.
3. Walk Through Logic: Explain iterating through the first array to populate the map, then traversing the second array to decrement counts and collect results when a match exists.
4. Analyze Complexity: Explicitly state Time Complexity is O(m + n) and Space Complexity is O(min(m, n)), justifying why this is efficient for large datasets typical at Amazon.
5. Dry Run: Trace a quick example like [1,2,2,1] and [2,2] to demonstrate how the logic handles duplicates without over-counting.
Key Points to Cover
- Explicitly stating the requirement to preserve duplicate counts based on the minimum frequency
- Selecting the Hash Map for O(1) average time lookups to achieve overall linear time complexity
- Optimizing space by building the frequency map on the smaller of the two arrays
- Demonstrating understanding of edge cases like empty arrays or no intersection
- Clearly articulating the trade-off between time and space complexity
Sample Answer
To solve the intersection of two arrays where duplicates matter, I would prioritize an approach that ensures linear time complexity while accurately tracking frequencies. First, I'd clarify that the output should contain…
Common Mistakes to Avoid
- Returning unique elements only instead of preserving the correct number of duplicates
- Using a nested loop approach resulting in inefficient O(n^2) time complexity
- Failing to decrement the hash map count after adding an element to the result
- Not considering which array to iterate first to minimize space complexity
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